Use of Smart Glasses (Assisted Reality) for Western Australian X-ray Operators' Continuing Professional Development: A Pilot Study.

Previous studies have explored use of smart glasses in telemedicine, but no study has investigated its use in teleradiography. The purpose of this study was to implement a six-month pilot program for Western Australian X-ray operators (XROs) to use smart glasses to obtain assisted reality support in their radiography practice from their supervising radiographers, and evaluate its effectiveness in terms of XROs' competence improvement and equipment usability. Pretest-posttest design with evaluation of the XROs' competence (including their X-ray image quality) and smart glasses usability by XROs in two remote centers and their supervising radiographers from two sites before and after the program using four questionnaire sets and X-ray image quality review was employed in this experimental study. Paired t-test was used for comparing mean values of the pre- and post-intervention pairs of 11-point scale questionnaire and image quality review items to determine any XROs' radiography competence improvements. Content analysis was used to analyze open questions about the equipment usability. Our study's findings based on 13 participants (11 XROs and 2 supervising radiographers) and 2053 X-ray images show that the assisted reality support helped to improve the XROs' radiography competence (specifically X-ray image quality), with mean post-intervention competence values of 6.16-7.39 (out of 10) and statistical significances (p < 0.001-0.05), and the equipment was considered effective for this purpose but not easy to use.

Comparative Study of Eclipse and RayStation Multi-Criteria Optimization-Based Prostate Radiotherapy Treatment Planning Quality.

Multi-criteria optimization (MCO) function has been available on commercial radiotherapy (RT) treatment planning systems to improve plan quality; however, no study has compared Eclipse and RayStation MCO functions for prostate RT planning. The purpose of this study was to compare prostate RT MCO plan qualities in terms of discrepancies between Pareto optimal and final deliverable plans, and dosimetric impact of final deliverable plans. In total, 25 computed tomography datasets of prostate cancer patients were used for Eclipse (version 16.1) and RayStation (version 12A) MCO-based plannings with doses received by 98% of planning target volume having 76 Gy prescription (PTV76D98%) and 50% of rectum (rectum D50%) selected as trade-off criteria. Pareto optimal and final deliverable plan discrepancies were determined based on PTV76D98% and rectum D50% percentage differences. Their final deliverable plans were compared in terms of doses received by PTV76 and other structures including rectum, and PTV76 homogeneity index (HI) and conformity index (CI), using a t-test. Both systems showed discrepancies between Pareto optimal and final deliverable plans (Eclipse: -0.89% (PTV76D98%) and -2.49% (Rectum D50%); RayStation: 3.56% (PTV76D98%) and -1.96% (Rectum D50%)). Statistically significantly different average values of PTV76D98%,HI and CI, and mean dose received by rectum (Eclipse: 76.07 Gy, 0.06, 1.05 and 39.36 Gy; RayStation: 70.43 Gy, 0.11, 0.87 and 51.65 Gy) are noted, respectively (p < 0.001). Eclipse MCO-based prostate RT plan quality appears better than that of RayStation.

Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy.

Given the high death rate caused by high-risk prostate cancer (PCa) (>40%) and the reliability issues associated with traditional prognostic markers, the purpose of this study is to investigate planning computed tomography (pCT)-based radiomics for the long-term prognostication of high-risk localized PCa patients who received whole pelvic radiotherapy (WPRT). This is a retrospective study with methods based on best practice procedures for radiomics research. Sixty-four patients were selected and randomly assigned to training (n = 45) and testing (n = 19) cohorts for radiomics model development with five major steps: pCT image acquisition using a Philips Big Bore CT simulator; multiple manual segmentations of clinical target volume for the prostate (CTVprostate) on the pCT images; feature extraction from the CTVprostate using PyRadiomics; feature selection for overfitting avoidance; and model development with three-fold cross-validation. The radiomics model and signature performances were evaluated based on the area under the receiver operating characteristic curve (AUC) as well as accuracy, sensitivity and specificity. This study's results show that our pCT-based radiomics model was able to predict the six-year progression-free survival of the high-risk localized PCa patients who received the WPRT with highly consistent performances (mean AUC: 0.76 (training) and 0.71 (testing)). These are comparable to findings of other similar studies including those using magnetic resonance imaging (MRI)-based radiomics. The accuracy, sensitivity and specificity of our radiomics signature that consisted of two texture features were 0.778, 0.833 and 0.556 (training) and 0.842, 0.867 and 0.750 (testing), respectively. Since CT is more readily available than MRI and is the standard-of-care modality for PCa WPRT planning, pCT-based radiomics could be used as a routine non-invasive approach to the prognostic prediction of WPRT treatment outcomes in high-risk localized PCa.

Special Issue: Advanced Research in Pediatric Radiology and Nuclear Medicine.

The importance of pediatric radiology and nuclear medicine is increasing [...].

Comparative Study of Plan Robustness for Breast Radiotherapy: Volumetric Modulated Arc Therapy Plans with Robust Optimization versus Manual Flash Approach.

A previous study investigated robustness of manual flash (MF) and robust optimized (RO) volumetric modulated arc therapy plans for breast radiotherapy based on five patients in 2020 and indicated that the RO was more robust than the MF, although the MF is still current standard practice. The purpose of this study was to compare their plan robustness in terms of dose variation to clinical target volume (CTV) and organs at risk (OARs) based on a larger sample size. This was a retrospective study involving 34 female patients. Their plan robustness was evaluated based on measured volume/dose difference between nominal and worst scenarios (ΔV/ΔD) for each CTV and OARs parameter, with a smaller difference representing greater robustness. Paired sample t-test was used to compare their robustness values. All parameters (except CTV ΔD98%) of the RO approach had smaller ΔV/ΔD values than those of the MF. Also, the RO approach had statistically significantly smaller ΔV/ΔD values (p < 0.001-0.012) for all CTV parameters except the CTV ΔV95% and ΔD98% and heart ΔDmean. This study's results confirm that the RO approach was more robust than the MF in general. Although both techniques were able to generate clinically acceptable plans for breast radiotherapy, the RO could potentially improve workflow efficiency due to its simpler planning process.

Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review.

Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years. However, no review article about GAN in pediatric radiology has been published yet. The purpose of this paper is to systematically review applications of GAN in pediatric radiology, their performances, and methods for their performance evaluation. Electronic databases were used for a literature search on 6 April 2023. Thirty-seven papers met the selection criteria and were included. This review reveals that the GAN can be applied to magnetic resonance imaging, X-ray, computed tomography, ultrasound and positron emission tomography for image translation, segmentation, reconstruction, quality assessment, synthesis and data augmentation, and disease diagnosis. About 80% of the included studies compared their GAN model performances with those of other approaches and indicated that their GAN models outperformed the others by 0.1-158.6%. However, these study findings should be used with caution because of a number of methodological weaknesses. For future GAN studies, more robust methods will be essential for addressing these issues. Otherwise, this would affect the clinical adoption of the GAN-based applications in pediatric radiology and the potential advantages of GAN could not be realized widely.

Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection and Diagnosis in Pediatric Radiology: A Systematic Review.

Artificial intelligence (AI)-based computer-aided detection and diagnosis (CAD) is an important research area in radiology. However, only two narrative reviews about general uses of AI in pediatric radiology and AI-based CAD in pediatric chest imaging have been published yet. The purpose of this systematic review is to investigate the AI-based CAD applications in pediatric radiology, their diagnostic performances and methods for their performance evaluation. A literature search with the use of electronic databases was conducted on 11 January 2023. Twenty-three articles that met the selection criteria were included. This review shows that the AI-based CAD could be applied in pediatric brain, respiratory, musculoskeletal, urologic and cardiac imaging, and especially for pneumonia detection. Most of the studies (93.3%, 14/15; 77.8%, 14/18; 73.3%, 11/15; 80.0%, 8/10; 66.6%, 2/3; 84.2%, 16/19; 80.0%, 8/10) reported model performances of at least 0.83 (area under receiver operating characteristic curve), 0.84 (sensitivity), 0.80 (specificity), 0.89 (positive predictive value), 0.63 (negative predictive value), 0.87 (accuracy), and 0.82 (F1 score), respectively. However, a range of methodological weaknesses (especially a lack of model external validation) are found in the included studies. In the future, more AI-based CAD studies in pediatric radiology with robust methodology should be conducted for convincing clinical centers to adopt CAD and realizing its benefits in a wider context.

Finetuned Super-Resolution Generative Adversarial Network (Artificial Intelligence) Model for Calcium Deblooming in Coronary Computed Tomography Angiography.

The purpose of this study was to finetune a deep learning model, real-enhanced super-resolution generative adversarial network (Real-ESRGAN), and investigate its diagnostic value in calcified coronary plaques with the aim of suppressing blooming artifacts for the further improvement of coronary lumen assessment. We finetuned the Real-ESRGAN model and applied it to 50 patients with 184 calcified plaques detected at three main coronary arteries (left anterior descending [LAD], left circumflex [LCx] and right coronary artery [RCA]). Measurements of coronary stenosis were collected from original coronary computed tomography angiography (CCTA) and Real-ESRGAN-processed images, including Real-ESRGAN-high-resolution, Real-ESRGAN-average and Real-ESRGAN-median (Real-ESRGAN-HR, Real-ESRGAN-A and Real-ESRGAN-M) with invasive coronary angiography as the reference. Our results showed specificity and positive predictive value (PPV) of the Real-ESRGAN-processed images were improved at all of the three coronary arteries, leading to significant reduction in the false positive rates when compared to those of the original CCTA images. The specificity and PPV of the Real-ESRGAN-M images were the highest at the RCA level, with values being 80% (95% CI: 64.4%, 90.9%) and 61.9% (95% CI: 45.6%, 75.9%), although the sensitivity was reduced to 81.3% (95% CI: 54.5%, 95.9%) due to false negative results. The corresponding specificity and PPV of the Real-ESRGAN-M images were 51.9 (95% CI: 40.3%, 63.5%) and 31.5% (95% CI: 25.8%, 37.8%) at LAD, 62.5% (95% CI: 40.6%, 81.2%) and 43.8% (95% CI: 30.3%, 58.1%) at LCx, respectively. The area under the receiver operating characteristic curve was also the highest at the RCA with value of 0.76 (95% CI: 0.64, 0.89), 0.84 (95% CI: 0.73, 0.94), 0.85 (95% CI: 0.75, 0.95) and 0.73 (95% CI: 0.58, 0.89), corresponding to original CCTA, Real-ESRGAN-HR, Real-ESRGAN-A and Real-ESRGAN-M images, respectively. This study proves that the finetuned Real-ESRGAN model significantly improves the diagnostic performance of CCTA in assessing calcified plaques.

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review.

Radiation dose optimization is particularly important in pediatric radiology, as children are more susceptible to potential harmful effects of ionizing radiation. However, only one narrative review about artificial intelligence (AI) for dose optimization in pediatric computed tomography (CT) has been published yet. The purpose of this systematic review is to answer the question "What are the AI techniques and architectures introduced in pediatric radiology for dose optimization, their specific application areas, and performances?" Literature search with use of electronic databases was conducted on 3 June 2022. Sixteen articles that met selection criteria were included. The included studies showed deep convolutional neural network (CNN) was the most common AI technique and architecture used for dose optimization in pediatric radiology. All but three included studies evaluated AI performance in dose optimization of abdomen, chest, head, neck, and pelvis CT; CT angiography; and dual-energy CT through deep learning image reconstruction. Most studies demonstrated that AI could reduce radiation dose by 36-70% without losing diagnostic information. Despite the dominance of commercially available AI models based on deep CNN with promising outcomes, homegrown models could provide comparable performances. Future exploration of AI value for dose optimization in pediatric radiology is necessary due to small sample sizes and narrow scopes (only three modalities, CT, positron emission tomography/magnetic resonance imaging and mobile radiography, and not all examination types covered) of existing studies.

Artificial Intelligence (Enhanced Super-Resolution Generative Adversarial Network) for Calcium Deblooming in Coronary Computed Tomography Angiography: A Feasibility Study.

BACKGROUND: The presence of heavy calcification in the coronary artery always presents a challenge for coronary computed tomography angiography (CCTA) in assessing the degree of coronary stenosis due to blooming artifacts associated with calcified plaques. Our study purpose was to use an advanced artificial intelligence (enhanced super-resolution generative adversarial network [ESRGAN]) model to suppress the blooming artifact in CCTA and determine its effect on improving the diagnostic performance of CCTA in calcified plaques. METHODS: A total of 184 calcified plaques from 50 patients who underwent both CCTA and invasive coronary angiography (ICA) were analysed with measurements of coronary lumen on the original CCTA, and three sets of ESRGAN-processed images including ESRGAN-high-resolution (ESRGAN-HR), ESRGAN-average and ESRGAN-median with ICA as the reference method for determining sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: ESRGAN-processed images improved the specificity and PPV at all three coronary arteries (LAD-left anterior descending, LCx-left circumflex and RCA-right coronary artery) compared to original CCTA with ESRGAN-median resulting in the highest values being 41.0% (95% confidence interval [CI]: 30%, 52.7%) and 26.9% (95% CI: 22.9%, 31.4%) at LAD; 41.7% (95% CI: 22.1%, 63.4%) and 36.4% (95% CI: 28.9%, 44.5%) at LCx; 55% (95% CI: 38.5%, 70.7%) and 47.1% (95% CI: 38.7%, 55.6%) at RCA; while corresponding values for original CCTA were 21.8% (95% CI: 13.2%, 32.6%) and 22.8% (95% CI: 20.8%, 24.9%); 12.5% (95% CI: 2.6%, 32.4%) and 27.6% (95% CI: 24.7%, 30.7%); 17.5% (95% CI: 7.3%, 32.8%) and 32.7% (95% CI: 29.6%, 35.9%) at LAD, LCx and RCA, respectively. There was no significant effect on sensitivity and NPV between the original CCTA and ESRGAN-processed images at all three coronary arteries. The area under the receiver operating characteristic curve was the highest with ESRGAN-median images at the RCA level with values being 0.76 (95% CI: 0.64, 0.89), 0.81 (95% CI: 0.69, 0.93), 0.82 (95% CI: 0.71, 0.94) and 0.86 (95% CI: 0.76, 0.96) corresponding to original CCTA and ESRGAN-HR, average and median images, respectively. CONCLUSIONS: This feasibility study shows the potential value of ESRGAN-processed images in improving the diagnostic value of CCTA for patients with calcified plaques.

Synchrotron radiation computed tomography assessment of calcified plaques and coronary stenosis with different slice thicknesses and beam energies on 3D printed coronary models.

BACKGROUND: To investigate the effect of different slice thicknesses and beam energies on the visualization and assessment of coronary artery stenosis caused by calcified plaques using synchrotron radiation computed tomography (CT) based on 3D printed coronary artery models. METHODS: Patient-specific 3D coronary models were created based on 3 sample coronary CT angiographic cases with calcified plaques in the left coronary arteries. In addition to the original significant coronary stenosis (>70%) shown on these CT images, stenoses of <50% and >90% were created in the segmented coronary models for simulation of different degrees of stenosis. The coronary lumen and calcification were printed with soft and rigid materials to simulate properties of coronary wall and calcified plaque, respectively. The models were scanned with synchrotron radiation CT with beam energies of 30, 40 and 50 keV and spatial resolution of 0.019×0.019×0.019 mm3 voxel size. Original high-resolution images were reconstructed with slice thicknesses of 0.095, 0.208, 0.302 and 0.491 mm to determine the effect of spatial resolution on plaque and coronary stenosis assessment based on 2D axial and 3D virtual intravascular endoscopy (VIE) images. RESULTS: Three coronary artery models were successfully printed with plaques placed in the coronary arteries to simulate different degrees of stenosis. 2D and 3D VIE images reconstructed with slice thicknesses of 0.095, 0.208 and 0.302 mm allowed for accurate assessment of coronary plaques and lumen stenosis with no significant differences (P>0.05). Synchrotron radiation CT images reconstructed with a slice thickness of 0.491 mm resulted in overestimation of coronary stenosis when compared to other images on 2D and 3D VIE views (<50% vs. 55-72%; 70-79% vs. 80-90%) with significant differences (P<0.05). Similarly, irregular plaque appearances were observed on 2D and 3D VIE images with a slice thickness of 0.491 mm when compared to others using thin slice thicknesses. The scanning protocol with beam energy of 30 keV provided optimal visualization of coronary lumen and plaque appearances. CONCLUSIONS: This study shows the feasibility of using 3D printed coronary artery models to simulate calcifications and different degrees of coronary stenosis. High resolution synchrotron radiation CT imaging with the 30 keV beam energy enables accurate assessment of coronary stenosis in the presence of calcification, thus highlighting the importance of high spatial resolution in the diagnosis of calcified coronary plaques.

Synchrotron radiation computed tomography versus conventional computed tomography for assessment of four types of stent grafts used for endovascular treatment of thoracic and abdominal aortic aneurysms.

BACKGROUND: To determine the accuracy of synchrotron radiation computed tomography (CT) for measurement of stent wire diameters for in vitro simulation of endovascular aneurysm repair by four different types of stent grafts when compared to conventional CT images. METHODS: This study was performed using an aorta model with implantation of four aortic stent grafts for endovascular treatment of thoracoabdominal and abdominal aortic aneurysms. The aorta model was scanned using synchrotron radiation CT with beam energies ranging from 60 to 90 keV with 10 keV increment at each scan and spatial resolution of 41.6 µm per pixel. Stent wire diameters were measured at the top and body regions of each stent graft based on 2-dimensional (2D) axial and 3-dimensional (3D) reconstruction images, with measurements compared to those obtained from 128-slice CT images which were acquired with slice thickness of 0.5 mm. RESULTS: Synchrotron radiation CT images clearly demonstrated stent graft details with accurate assessment of stent wire diameters, with measurements at the top of stent grafts (between 0.32±0.02 and 0.47±0.02 mm) similar to the actual diameters (between 0.32±0.01 and 0.48±0.01 mm) when the beam energies of 70 and 80 keV were used, regardless of the types of stent grafts assessed. A beam energy of 60 keV resulted in stent wires thicker than the actual sizes, although this did not reach statistical significance (P=0.07-0.29), while the beam energy of 90 keV led to stent wires smaller than the actual sizes at the top (P=0.16) and body region (P=0.02) of stent grafts on 2D axial images. The stent wire sizes measured at the body region of stent grafts on 3D synchrotron radiation images (between 0.19±0.02 and 0.43±0.02 mm) were significantly smaller than the actual diameters (P=0.02-0.04). Stent wires were overestimated on conventional CT images with diameters more than 2-fold larger than the actual sizes (P=0.007-0.03) at both top and body regions of all four stent grafts. CONCLUSIONS: This study further confirms the accuracy of high-resolution synchrotron radiation CT in image visualization and size measurement of different aortic stent grafts with measured wire diameters similar to the actual ones, thus allowing for more accurate assessment of stent wire details for endovascular repair of aortic aneurysms.

Use of Synchrotron Radiation to Accurately Assess Cross-Sectional Area Reduction of the Aortic Branch Ostia Caused by Suprarenal Stent Wires.

PURPOSE: To compare in vivo the use of synchrotron radiation to computed tomography angiography (CTA) for the measurement of cross-sectional area (CSA) reduction of the aortic branch ostia caused by suprarenal stent-graft wires. METHODS: This study was performed with a Zenith stent-graft placed in a phantom of the human aorta to simulate treatment of abdominal aortic aneurysm. Synchrotron radiation scans were performed using beam energies between 40 and 100 keV and spatial resolution of 19.88 µm per pixel. CSA reduction of the aortic branch ostia by suprarenal stent wires was calculated based on these exposure factors and compared with measurements from CTA images acquired on a 64-row scanner with slice thicknesses of 1.0, 1.5, and 2.0 mm. RESULTS: Images acquired with synchrotron radiation showed <10% of the CSA occupied by stent wires when a single wire crossed a renal artery ostium and <20% for 2 wires crossing a renovisceral branch ostium. The corresponding areas ranged from 24% to 25% for a single wire and from 40% to 48% for double wires crossing the branch ostia when measured on CT images. The stent wire was accurately assessed on synchrotron radiation with a diameter between 0.38±0.01 and 0.53±0.03 mm, which is close to the actual size of 0.47±0.01 mm. The wire diameter measured on CT images was greatly overestimated (1.15±0.01 to 1.57±0.02 mm). CONCLUSION: CTA has inferior spatial resolution that hinders accurate assessment of CSA reduction. This experiment demonstrated the superiority of synchrotron radiation over CTA for more accurate assessment of aortic stent wires and CSA reduction of the aortic branch ostia.

Coronary CT Angiography in Heavily Calcified Coronary Arteries: Improvement of Coronary Lumen Visualization and Coronary Stenosis Assessment With Image Postprocessing Methods.

To compare the diagnostic value of coronary CT angiography (CCTA) with use of 2 image postprocessing methods (CCTA_S) and (CCTA_OS) and original data (CCTA_O) for the assessment of heavily calcified plaques. Fifty patients (41 men, 9 women; mean age 61.9 years ± 9.1) with suspected coronary artery disease who underwent CCTA and invasive coronary angiography (ICA) examinations were included in the study. Image data were postprocessed with "sharpen" and smooth reconstruction algorithms in comparison with the original data without undergoing any image postprocessing to determine the effects on suppressing blooming artifacts due to heavy calcification in the coronary arteries. Minimal lumen diameter and degree of stenosis were measured and compared between CCTA_S, CCTA_OS, and CCTA_O with ICA as the reference method. The area under the curve (AUC) by receiver-operating characteristic curve analysis (ROC) was also compared among these 3 CCTA techniques. On a per-vessel assessment, the sensitivity, specificity, positive predictive value and negative predictive value, and 95% confidence interval (CI) were 100% (95% CI: 89%, 100%), 33% (95% CI: 22%, 45%), 41% (95% CI: 30%, 53%), 100% (95% CI: 85%, 100%) for CCTA_O, 94% (95% CI: 79%, 99%), 66% (95% CI: 54%, 77%), 57% (95% CI: 43%, 70%), and 95% (95% CI: 85%, 99%) for CCTA_S, 94% (95% CI: 79%, 99%), 44% (95% CI: 32%, 57%), 44% (95% CI: 32%, 57%), and 97% (95% CI: 79%, 99%) for CCTA_OS, respectively. The AUC by ROC curve analysis for CCTA_S showed significant improvement for detection of >50% coronary stenosis in left anterior descending coronary artery compared to that of CCTA_OS and CCTA_O methods (P < 0.05), with no significance differences for detection of coronary stenosis in the left circumflex and right coronary arteries (P > 0.05).CCTA with "sharpen" reconstruction reduces blooming artifacts from heavy calcification, thus, leading to significant improvement of specificity and positive predictive value of CCTA in patients with heavily calcified plaques. However, specificity is still moderate and additional functional imaging may be needed.

Study of Radiologic Technologists' Perceptions of Picture Archiving and Communication System (PACS) Competence and Educational Issues in Western Australia.

Although the implementation of picture archiving and communication system (PACS) could increase productivity of radiology departments, this depends on factors such as the PACS competence of radiologic technologists (RTs). The purpose of this study was to investigate the RTs' perceptions of PACS competence and educational issues in Western Australia (WA). A hardcopy questionnaire was distributed to WA RTs for obtaining their perceptions of PACS competence and educational issues. Descriptive (percentage of frequency, mean and standard deviation) and inferential statistics (t test and analysis of variance) were used to analyze the responses of the multiple choice and five-point scale questions from the returned questionnaires. The questionnaire response rate was 57.7% (173 out of 300). The mean values of all PACS competence questions except questions 2e-g are in the range of 3.9-4.9, i.e., around competent to very competent. Participants indicated they received adequate PACS training (mean 3.8). Statistically significant variables influencing RTs' perceptions of their PACS competence and educational issues including the age (p < 0.01), gender (p < 0.05), years of practice (p < 0.005-0.05), primary duty (p < 0.05), medical imaging qualification (p < 0.001), general computer skills (p < 0.001), and type of PACS education received (p < 0.001-0.05). The WA RTs indicated that they were competent in using the modality workstation, PACS and radiology information system, and received adequate training. However, future PACS education programs should be tailored to different RTs' groups. For example, multiple training modules might be necessary to support the PACS competence development of older RTs and those with lower general computer literacy.

Development of an online automatic diagnostic reference levels management system for digital radiography: a pilot experience.

The diagnostic reference levels (DRLs) concept is a methodology proposed by the International Commission on Radiological Protection (ICRP) for identifying any unusual high patient doses involved in radiological examinations. However, some challenges are anticipated in the DRLs concept including resource demand for running the audit cycle by individual imaging departments and availability of DRLs. The objective of this study was to develop an online automatic DRLs management system for digital radiography (DR) with the aim of addressing the challenges of the DRLs obligation. An online automatic DRLs management system for DR composed of freeware was developed. The system was tested with 75 DR images. This pilot experience shows that the system successfully addresses the challenges in the DRLs management, i.e. resource demand for running the audit cycle by individual imaging departments and availability of DRLs. It can provide at a low cost an efficient and effective solution to the implementation of regular audits of patient doses using DR in busy clinical departments. It can also contribute to the development of DRLs at local and national levels. In this way, any unacceptable radiological practice (examination used unjustified high radiation dose) can be identified.

Development of an online automatic computed radiography dose data mining program: a preliminary study.

Recent studies have reported the computed radiography (CR) dose creep problem and therefore the need to have monitoring processes in place in clinical departments. The objective of this study is to provide a better technological solution to implement a regular CR dose monitoring process. An online automatic CR dose data mining program which can be applied to different systems was developed based on freeware and existing softwares in the Picture Archiving and Communication System (PACS) server. The program was tested with 69 CR images. This preliminary study shows that the program addresses the major weaknesses of some existing studies including involvement of manual procedures in the monitoring process and being only applicable to a single manufacturer's CR images. The proposed method provides an efficient and effective solution to implement a CR dose monitoring program regularly in busy clinical departments to regulate the dose creep problem so as to reinforce the 'As Low As Reasonably Achievable' (ALARA) principle.

Development of a web database portfolio system with PACS connectivity for undergraduate health education and continuing professional development.

Increasingly, the use of web database portfolio systems is noted in medical and health education, and for continuing professional development (CPD). However, the functions of existing systems are not always aligned with the corresponding pedagogy and hence reflection is often lost. This paper presents the development of a tailored web database portfolio system with Picture Archiving and Communication System (PACS) connectivity, which is based on the portfolio pedagogy. Following a pre-determined portfolio framework, a system model with the components of web, database and mail servers, server side scripts, and a Query/Retrieve (Q/R) broker for conversion between Hypertext Transfer Protocol (HTTP) requests and Q/R service class of Digital Imaging and Communication in Medicine (DICOM) standard, is proposed. The system was piloted with seventy-seven volunteers. A tailored web database portfolio system (http://radep.hti.polyu.edu.hk) was developed. Technological arrangements for reinforcing portfolio pedagogy include popup windows (reminders) with guidelines and probing questions of 'collect', 'select' and 'reflect' on evidence of development/experience, limitation in the number of files (evidence) to be uploaded, the 'Evidence Insertion' functionality to link the individual uploaded artifacts with reflective writing, capability to accommodate diversity of contents and convenient interfaces for reviewing portfolios and communication. Evidence to date suggests the system supports users to build their portfolios with sound hypertext reflection under a facilitator's guidance, and with reviewers to monitor students' progress providing feedback and comments online in a programme-wide situation.